Visulisation of secretory clusters
Zhiyuan Hu
12/11/2019
Summary
In this part, we are going to visualise the secretory cells after clustering.
It will include the figures we presented in our manuscript.
Visualisation
Scatter plot of secretory clusters
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Scatter plots of fresh FTE cells
Plot UMAP of all fresh cells
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## Ciliated Secretory
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Heatmap
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Violin plots of marker genes
Cluster 4 is characterized by upregulation of major histocompatibility complex (MHC) Class II genes (e.g. HLA-DQA1, HLA-DPA1 and HLA-DPB1), cytokeratins (KRT17, KRT5 and KRT23) and aldehyde dehydrogenases (e.g. ALDH1A1 and ALDH3B2)
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Technical
## R version 3.5.2 (2018-12-20)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.4
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] bindrcpp_0.2.2 Seurat_2.3.4
## [3] Matrix_1.2-15 cowplot_0.9.4
## [5] DoubletFinder_2.0.1 reshape2_1.4.3
## [7] scales_1.0.0 dplyr_0.7.8
## [9] edgeR_3.24.3 limma_3.38.3
## [11] scater_1.10.1 ggplot2_3.2.0.9000
## [13] SingleCellExperiment_1.4.1 SummarizedExperiment_1.12.0
## [15] DelayedArray_0.8.0 BiocParallel_1.16.5
## [17] matrixStats_0.54.0 Biobase_2.42.0
## [19] GenomicRanges_1.34.0 GenomeInfoDb_1.18.1
## [21] IRanges_2.16.0 S4Vectors_0.20.1
## [23] BiocGenerics_0.28.0
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## loaded via a namespace (and not attached):
## [1] snow_0.4-3 backports_1.1.3
## [3] Hmisc_4.2-0 plyr_1.8.4
## [5] igraph_1.2.3 lazyeval_0.2.1
## [7] splines_3.5.2 digest_0.6.18
## [9] foreach_1.4.4 htmltools_0.3.6
## [11] viridis_0.5.1 lars_1.2
## [13] gdata_2.18.0 magrittr_1.5
## [15] checkmate_1.9.1 cluster_2.0.7-1
## [17] mixtools_1.1.0 ROCR_1.0-7
## [19] R.utils_2.7.0 colorspace_1.4-0
## [21] xfun_0.4 jsonlite_1.6
## [23] crayon_1.3.4 RCurl_1.95-4.11
## [25] bindr_0.1.1 zoo_1.8-4
## [27] survival_2.43-3 iterators_1.0.10
## [29] ape_5.2 glue_1.3.0
## [31] gtable_0.2.0 zlibbioc_1.28.0
## [33] XVector_0.22.0 kernlab_0.9-27
## [35] Rhdf5lib_1.4.2 prabclus_2.2-7
## [37] DEoptimR_1.0-8 HDF5Array_1.10.1
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## [49] proxy_0.4-22 mclust_5.4.2
## [51] SDMTools_1.1-221 Formula_1.2-3
## [53] umap_0.1.0.3 tsne_0.1-3
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## [57] gplots_3.0.1.1 RColorBrewer_1.1-2
## [59] fpc_2.1-11.1 acepack_1.4.1
## [61] modeltools_0.2-22 ica_1.0-2
## [63] pkgconfig_2.0.2 R.methodsS3_1.7.1
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## [91] compiler_3.5.2 rstudioapi_0.9.0
## [93] png_0.1-7 beeswarm_0.2.3
## [95] lsei_1.2-0 statmod_1.4.30
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## [99] lattice_0.20-38 trimcluster_0.1-2.1
## [101] pillar_1.3.1 lmtest_0.9-36
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## [131] base64enc_0.1-3 ggbeeswarm_0.6.0